A new technique uses high-throughput sequencing, instead of a microscope, to obtain ultra-high-resolution images of gene expression from a tissue slide.
In a major scientific leap, University of Queensland researchers have created a quantum microscope that can reveal biological structures that would otherwise be impossible to see. This paves the way for applications in biotechnology, and could extend far beyond this into areas ranging from navigation to medical imaging. The microscope is powered by the science of quantum entanglement, an effect Einstein described as "spooky interactions at a distance." Professor Warwick Bowen, from UQ's Quantum Optics Lab and the ARC Centre of Excellence for Engineered Quantum Systems (EQUS), said it was the first entanglement-based sensor with performance beyond the best possible existing technology. "This breakthrough will spark all sorts of new technologies -- from better navigation systems to better MRI machines, you name it," Professor Bowen said. "Entanglement is thought to lie at the heart of a quantum revolution.
The challenges imposed on healthcare systems are unprecedented, and medical operations worldwide are crippled. Nevertheless, the need for remote pathological evaluation and disease diagnosis amidst the COVID-19 pandemic has brought digital pathology into the spotlight, and the speed of clinical adoption of digital pathology technologies is increasing at an unprecedented rate.
Recently, mass spectrometry-based assays have gained increasing popularity amongst clinical diagnostic laboratories and emerged as a promising tool for modern pathology owing to their multiplexing abilities and exceptional analytical performance.
From diagnosing disease, to personalizing treatment, to predicting complications from surgery, AI could become as integral to patient care in the future as imaging and laboratory tests are today. But as University of Washington researchers discovered, AI models — like humans — have a tendency to look for shortcuts. In the case of AI-assisted disease detection, these shortcuts could lead to diagnostic errors if deployed in clinical settings.
Electrical engineers at the University of California San Diego developed a technology that improves the resolution of an ordinary light microscope so that it can be used to directly observe finer structures and details in living cells.
In January this year, Roche launched two new digital pathology algorithms aimed at improving the diagnosis of breast cancer. These automated algorithms, uPath HER2 (4B5) and uPath Dual ISH, expand Roche’s digital pathology portfolio and provide further tools to assist in determining the most appropriate personalized treatment strategy for individual breast cancer patients. Why breast cancer? Globally [...]
Goal to develop regulatory-approved software products and services based on AI-generated insights from Quest's digital pathology diagnostic data
An international research team has trained artificial intelligence algorithms to detect blood cancer, lung diseases and COVID-19 in molecular and image data stored in a decentralized fashion using "swarm learning." This approach has advantage over conventional methods since it inherently provides privacy preservation technologies, which facilitates cross-site analysis of scientific and medical data.
Single-molecule counting is a fast and robust single-molecule counting technology offering a high dynamic range, accuracy, reproducibility, and sensitivity.
